Evolutionary-statistical system for uncertainty reduction problems in wildfires

Fire modelling is used by engineers and scientists to understand and to predict possible fire behaviour. Empirical, semi-empirical, and physical models have been developed to predict wildfire behaviour. Any of these can be used to develop simulators and tools for preventing and fighting wildfires. However, in many cases the models present a series of limitations related to the need for a large number of input parameters. Moreover, such parameters often have some degree of uncertainty due to the impossibility of getting all of them in real time. Consequently, these values have to be estimated from indirect measurements, which negatively impacts on the output of the model. In this paper we show a method which takes advantage of the computational power provided by High Performance Computing to improve the quality of the output of the model. This method combines Statistical Analysis with Parallel Evolutionary Algorithms. Besides, we compare this method with a previous version which did not use evolutionary algorithms.Eje: Workshop Procesamiento distribuido y paralelo (WPDP)Red de Universidades con Carreras en Informática (RedUNCI

A review of machine learning applications in wildfire science and management

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.Comment: 83 pages, 4 figures, 3 table

Prediction Time Assessment in a DDDAS for Natural Hazard Management: Forest Fire Study Case ✩

This work faces the problem of quality and prediction time assessment in a Dynamic Data Driven Application System (DDDAS) for predicting natural hazard evolution. In particular, we used forest fire spread prediction as a case study to show the applicability of the methodology. The improvement on the prediction quality when using a two-stage DDDAS prediction framework has been widely proved. The two-stages DDDAS has a first phase where an adjustment of the input data is performed in order to be applied in the second phase, the prediction. This paper is focused on defining a new methodology for prediction time assessment under this kind of prediction environments by evaluating, in advance, how a certain combination of simulator, computational resources, adjustment strategy, and frequency of data acquisition will perform, in terms of prediction time. Since the time incurred in the hazard simulation is a crucial part of the whole prediction time, we have defined a methodology to classify the simulator’s execution time using Artificial Intelligence techniques allowing us to determine upper bounds for the DDDAS prediction time depending on the particular input parameter setting. This methodology can be extrapolated to any DDDAS for predicting natural hazards evolution, which uses the two-stage prediction scheme as a working framework. Keywords

Simulación paramétrica paralela. Aplicación a modelos de predicción de inundaciones.

El modelado y la simulación de inundaciones provocadas por el desborde de ríos brinda sistemas computacionales para el estudio y la predicción de estos fenómenos naturales, con el objetivo de pronosticar su comportamiento. Estos sistemas necesitan tomar gran cantidad de datos de entrada para aumentar su precisión, como también deben generar múltiples escenarios para cubrir todas las situaciones de riesgo. Por esto, son de cómputo intensivo y pueden tomar días de procesamiento hasta lograr resultados. A este problema se le suma la falta de certeza en los valores de los datos de entrada del proceso. Mediante la programación paralela y los avances en cómputo de alto rendimiento en clusters de computadoras, se pretende atenuar el problema de la incertidumbre de los datos de entrada y optimizar el proceso de predicción mediante la simulación de múltiples escenarios. Con este trabajo se pretende desarrollar una metodología para optimizar la predicción de inundaciones provocadas por el desborde de ríos, en principio de llanuras o planicies, y en particular en la Cuenca del Río Salado o en el Paraná Medio.Eje: Procesamiento Concurrente, Paralelo y DistribuidoRed de Universidades con Carreras en Informática (RedUNCI

Simulación paramétrica paralela. Aplicación a modelos de predicción de inundaciones.

El modelado y la simulación de inundaciones provocadas por el desborde de ríos brinda sistemas computacionales para el estudio y la predicción de estos fenómenos naturales, con el objetivo de pronosticar su comportamiento. Estos sistemas necesitan tomar gran cantidad de datos de entrada para aumentar su precisión, como también deben generar múltiples escenarios para cubrir todas las situaciones de riesgo. Por esto, son de cómputo intensivo y pueden tomar días de procesamiento hasta lograr resultados. A este problema se le suma la falta de certeza en los valores de los datos de entrada del proceso. Mediante la programación paralela y los avances en cómputo de alto rendimiento en clusters de computadoras, se pretende atenuar el problema de la incertidumbre de los datos de entrada y optimizar el proceso de predicción mediante la simulación de múltiples escenarios. Con este trabajo se pretende desarrollar una metodología para optimizar la predicción de inundaciones provocadas por el desborde de ríos, en principio de llanuras o planicies, y en particular en la Cuenca del Río Salado o en el Paraná Medio.Eje: Procesamiento Concurrente, Paralelo y DistribuidoRed de Universidades con Carreras en Informática (RedUNCI

Desarrollo de aplicaciones paralelo/distribuidas orientadas a la predicción de incendios forestales

La problemática existente a raíz de la falta de exactitud presente en los parámetros de entrada en cualquier modelo científico o físico, puede producir consecuencias dramáticas en la salida del mismo si se trata éste de algún sistema crítico. Además, al citado problema deben sumarse las limitaciones impuestas por los propios modelos, las restricciones que agregan las soluciones numéricas y, por qué no, las provenientes de las propias implementaciones y versiones informáticas. Por tal motivo, resulta de gran interés el desarrollo de métodos y herramientas informáticos que se enfoquen en el tratamiento de la incertidumbre de los valores de entrada para lograr así una predicción lo más confiable posible por parte del modelo en cuestión. En el caso concreto de los incendios forestales, la simulación de la propagación constituye un desafío desde el punto de vista computacional, dada la complejidad que involucran los modelos, los métodos numéricos y la administración de los recursos. La clase de métodos que aborda nuestra línea de investigación constituye una importante herramienta para la prevención y predicción, dado que provee información acerca del posible comportamiento del fuego y las zonas que corren mayor peligro.Eje: Procesamiento Distribuido y ParaleloRed de Universidades con Carreras en Informática (RedUNCI

Applying probability theory for the quality assessment of a wildfire spread prediction framework based on genetic algorithms

This work presents a framework for assessing how the existing constraints at the time of attending an ongoing forest fire affect simulation results, both in terms of quality (accuracy) obtained and the time needed to make a decision. In the wildfire spread simulation and prediction area, it is essential to properly exploit the computational power offered by new computing advances. For this purpose, we rely on a two-stage prediction process to enhance the quality of traditional predictions, taking advantage of parallel computing. This strategy is based on an adjustment stage which is carried out by a well-known evolutionary technique: Genetic Algorithms. The core of this framework is evaluated according to the probability theory principles. Thus, a strong statistical study is presented and oriented towards the characterization of such an adjustment technique in order to help the operation managers deal with the two aspects previously mentioned: time and quality. The experimental work in this paper is based on a region in Spain which is one of the most prone to forest fires: El Cap de Creus

The Burning Bush: Linking LiDAR-derived Shrub Architecture to Flammability

Light detection and ranging (LiDAR) and terrestrial laser scanning (TLS) sensors are powerful tools for characterizing vegetation structure and for constructing three-dimensional (3D) models of trees, also known as quantitative structural models (QSM). 3D models and structural traits derived from them provide valuable information for biodiversity conservation, forest management, and fire behavior modeling. However, vegetation studies and 3D modeling methodologies often only focus on the forest canopy, with little attention given to understory vegetation. In particular, 3D structural information of shrubs is limited or not included in fire behavior models. Yet, understory vegetation is an important component of forested ecosystems, and has an essential role in determining fire behavior. In this dissertation, I explored the use of TLS data and quantitative structure models to model shrub architecture in three related studies. In the first study, I present a semi-automated methodology for reconstructing architecturally different shrubs from TLS LiDAR. By investigating shrubs with different architectures and point cloud densities, I showed that occlusion, shrub complexity, and shape greatly affect the accuracy of shrub models. In my second study, I assessed the 3D architectural drivers of understory flammability by evaluating the use of architectural metrics derived from the TLS point cloud and 3D reconstructions of the shrubs. I focused on eight species common in the understory of the fire-prone longleaf pine forest ecosystem of the state of Florida, USA. I found a general tendency for each species to be associated with a unique combination of flammability and architectural traits. Novel shrub architectural traits were found to be complementary to the direct use of TLS data and improved flammability predictions. The inherent complexity of shrub architecture and uncertainty in the TLS point cloud make scaling up from an individual shrub to a plot level a challenging task. Therefore, in my third study, I explored the effects of lidar uncertainty on vegetation parameter prediction accuracy. I developed a practical workflow to create synthetic forest stands with varying densities, which were subsequently scanned with simulated terrestrial lidar. This provided data sets quantitatively similar to those created by real-world LiDAR measurements, but with the advantage of exact knowledge of the forest plot parameters, The results showed that the lidar scan location had a large effect on prediction accuracy. Furthermore, occlusion is strongly related to the sampling density and plot complexity. The results of this study illustrate the potential of non-destructive lidar approaches for quantifying shrub architectural traits. TLS, empirical quantitative structural models, and synthetic models provide valuable insights into shrub structure and fire behavior

Review and new methodological approaches in human-caused wildfire modeling and ecological vulnerability: Risk modeling at mainland Spain

En las últimas décadas, las autoridades en materia de incendios han fomentado la investigación acerca de los factores desencadenantes del fuego, parámetro decisivo para lograr un entendimiento mayor de los patrones de la ocurrencia de incendios y mejorar las medidas preventivas. Existe por tanto una necesidad de mejorar y actualizar los enfoques metodológicos para el modelado de incendios forestales, teniendo en cuenta no sólo algoritmos innovadores, sino también la mejora y/o superación de los métodos clásicos de regresión. Por otra parte, es también imprescindible fomentar la evaluación de los posibles daños potenciales en los ecosistemas naturales, promoviendo así la conservación de los servicios de valor económico, ambiental, cultural y estético que éstos proporcionan a la sociedad. El objetivo principal de esta tesis doctoral es explorar nuevos métodos para el modelado de la causalidad humana en incendios forestales así como de los efectos adversos sobre las comunidades vegetales potencialmente afectadas. El modelado de la causalidad humana se ha realizado a partir de métodos de aprendizaje artificial y de técnicas de regresión geográficamente ponderada. Estas técnicas permiten por una parte el ajuste de modelos de probabilidad de ocurrencia espacialmente explícitos y, por otra, el estudio de la variabilidad espacial de los factores explicativos. La estimación de la vulnerabilidad de la vegetación frente al fuego, se ha llevado a cabo utilizando un enfoque cuantitativo, que permita superar los métodos existentes, que, si bien pueden ser útiles en algunas áreas de la gestión del territorio, son inadecuados para otros tipos de análisis, tales como la estimación de las pérdidas económicas inducidas por el fuego como consecuencia de la interrupción de los servicios ambientales (por ejemplo, la madera, la caza, y la recolección de setas). Para abordar el análisis de la vulnerabilidad se propone un método basado en la estimación del tiempo de recuperación de las comunidades vegetales tras el fuego, desarrollado mediante álgebra de mapas en entorno SIG. Los resultados indican que la utilización de métodos de aprendizaje artificial (concretamente el algoritmo Random Forest) supone una mejora sustancial respecto a los métodos clásicos de regresión, si bien parece que existe cierta incertidumbre en los modelos desarrollados, relacionada principalmente con la calidad de los datos de ocurrencia. Además, la aplicación de modelos GWR ha revelado la existencia de una elevada heterogeneidad espacial en la relación y capacidad explicativa de los factores relacionados con la ocurrencia de incendios con origen antrópico. Por otra parte, la aplicación del modelo propuesto para la estimación cuantitativa de la vulnerabilidad ecológica sugiere que la capacidad de respuesta de la vegetación se encuentra estrechamente relacionada con la estrategia reproductiva de las especies afectadas.Over the last decades, authorities responsible on forest fire have encouraged research on fire triggering factors, recognizing this as a critical point to achieve a greater understanding of fire occurrence patterns and improve preventive measures. There is therefore a need to improve and update the methodological approaches for modeling forest fires, taking into account not only innovative algorithms, but also improving and/or overcoming classical regression methods. On the other hand it is also essential to encourage the assessment of potential damage on natural ecosystems, promoting the conservation of its economic, environmental, cultural and aesthetic assets they provide to society. The main objective of this PhD thesis is to explore new methods for modeling human causality in forest fires and adverse effects on the plant communities potentially affected. Human causality modeling was carried out from machine learning methods and geographically weighted regression techniques. These procedures allow the adjustment spatially explicit probability models of occurrence and, secondly, the study of the spatial variability of wildfire explanatory factors. The estimation of the vulnerability of vegetation to fire was carried out using a quantitative approach to overcome current methods, which, while they may be useful in some areas of land management, are inadequate for other types of analysis, such as estimating economic losses induced by interrupting ecosystem services (e.g., wood, hunting, and gathering mushrooms). To address the vulnerability a method based on evaluating the recovery time of plant communities after the fire using a GIS map algebra approach is proposed. The results suggest that the use of machine learning methods (specifically the Random Forest algorithm) represents a substantial improvement over traditional methods of regression, although it appears that there is some uncertainty in the models, primarily related to the quality of ignition. Furthermore, the application of GWR models has revealed the existence of a high spatial heterogeneity in the relationship and explanatory power of the factors related to the occurrence of anthropogenic fires. Moreover, the application of the proposed model for the quantitative estimation of ecological vulnerability suggests that the responsiveness of vegetation is closely related to the reproductive strategy of the fire-affected species